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setwd("D:/R program")
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#values needed
K= 1.38064852*(10)^-23 #m2 kg/ s2 K boltzmann constant
mu= 1.126*(10)^-3 #kg/m s dynamic viscosity in 18C
v= 1.099*(10)^-6 #m2/s kinematic viscosity in 18C
Reh_calc= 2.3E-6 #in m radius Ehux
Reh_naked= 1.8E-6 #in m radius Ehux
Rehv= 90*(10)^-9 #in m radius virus
Temp = 18+273.15 #temp in kelvin, here assuming 18C
Den_OcM = 1.05 #g/cm3 density organic cell matter
Den_CH2O= 1.025 #g/cm3 density seawater at 18C
hostnum <- (10)^3
virnum <- (10)^4
require (ggplot2)
require(plotly)
require(grid)
require(ggthemes)
require (dplyr)
require(plyr)
source ("theme_Publication.R")
source("resizewin.R")
resize.win(12,9)
Error in dev.off() : cannot shut down device 1 (the null device)
for Brownian motion
#Brownian motion (BM)
#1. make a data frame
BM <- data.frame (group= c("naked", "calcified"), rad= c(1.8E-6, 2.3E-6))
#2. calculate beta (beta)
BM$beta_s <- (2*(K*(10)^4)*Temp*(((BM$rad+Rehv)*100)^2))/((3*mu*10)*(BM$rad*Rehv*1e4)) #m3/s
BM$beta_d <- BM$beta_s*86400 #to cm3/day
# go back to this later
#3. calculate encounters (E)
BM$E <- BM$beta_d*hostnum
BM$E_HV <- BM$beta_d*virnum*hostnum
BM
Differential settling (DS)
#Differential settling (DS)
#1. read in PIC data
library(readr) #always use readr not baseR
setwd("D:/R program")
The working directory was changed to D:/R program inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the working directory for notebook chunks.
PIC <- read_csv("Postdoc-R/CSV Files/PIC.csv")
Parsed with column specification:
cols(
Strain = col_character(),
Replicate = col_integer(),
TC = col_double(),
AC = col_double(),
Cellcount = col_double()
)
PIC$Strain <- as.factor(PIC$Strain)
PIC$Replicate <- as.factor(PIC$Replicate)
#certain changes in data.table API made calculating inside the list data.table to not work
#2. calculate PIC
PIC$PIC <- PIC$TC-PIC$AC
PIC$PICpercell <- (PIC$PIC/PIC$Cellcount)*(10)^-3#in g
PIC$PICpercellpg <- PIC$PICpercell*1e12
ggplotly(ggplot(data=PIC, aes(x=Strain, y=PICpercellpg)) + geom_boxplot()+geom_point(size=2) +theme_Publication())
#3. calculate density of cells (den)
PIC <- mutate(PIC, group = ifelse(PICpercellpg < 4 , "naked", "calcified"))
ggplotly(ggplot(data=PIC, aes(x=group, y=PICpercellpg)) + geom_boxplot()+geom_point(size=2, aes(color=Strain))+
theme_Publication())
plotly.js does not (yet) support horizontal legend items
You can track progress here:
https://github.com/plotly/plotly.js/issues/53
PIC <- mutate(PIC, rad = ifelse(group == "naked" , 1.8E-6, 2.3E-6)) #in m
PIC$volume <- (4/3)*pi*(PIC$rad*100)^3 #in cm3
PIC$Den_cell <- PIC$PICpercell/PIC$volume #g/cm3
PIC$Den_celltotal <- PIC$Den_cell+Den_OcM
ggplotly(ggplot(data=PIC, aes(x=Strain, y=Den_celltotal, color=group)) + geom_boxplot()+geom_point(size=2)
+theme_Publication())
plotly.js does not (yet) support horizontal legend items
You can track progress here:
https://github.com/plotly/plotly.js/issues/53
#some strains that are "naked" have PIC<2. I chose to ignore this since in the lm model I do not use
#strain as a factor, rather data is treated as a whole (e.g., no grouping)
#4. calculate sinking velocity of cells
PIC$SinkVel <- ((2*((PIC$rad*100)^2)*(981)*(PIC$Den_celltotal-Den_CH2O))/(9*(mu*10)))*864 #meter per day
#g is converted to per day, 864 is the one that converts cm/s to m/day
#plot sinking velocity vs calcification
ggplot(data=PIC, aes(x=PICpercellpg, y=SinkVel, color=Strain, shape=group)) + geom_point(size=5)+theme_Publication()+
labs(y = expression("Sinking velocity"~("m"~day^-1)), x = expression("PIC"~cell^-1)) +
scale_y_log10(
breaks = scales::trans_breaks("log10", function(x) 10^x, n=2),
labels = scales::trans_format("log10", scales::math_format(10^.x))) + annotation_logticks(sides="l")

#5. calculate sinkvel of viruses
Den_virus <- 1.09 #data from Ben D. fresh EhV-207 density. old density of EhV-207 is 1.19
Ehv_SinkVel <- ((2*((Rehv*100)^2)*(981)*(Den_virus-Den_CH2O))/(9*(mu*10)))*864 #equals to 0
#6. calculate beta kernels
PIC$beta_s <- pi*(((PIC$rad+Rehv)*100)^2)*(abs((PIC$SinkVel-Ehv_SinkVel)/864)) #in encounters cm3/s
PIC$beta_d <- PIC$beta_s*86400 #in cm3/day
Sinkvelbeta.plot<- ggplot(data=PIC, aes(x=SinkVel, y=beta_d, color=Strain, shape=group)) + geom_point(size=5)+
theme_Publication()+
labs(x = expression("Sinking velocity"~("m"~day^-1)), y = expression(beta~("Encounters" ~ cm^3~day^-1))) +
scale_y_log10(
breaks = scales::trans_breaks("log10", function(x) 10^x, n=2),
labels = scales::trans_format("log10", scales::math_format(10^.x))) + annotation_logticks(sides="l")
Sinkvelbeta.plot #change ticks

ggplotly(Sinkvelbeta.plot)
geom_GeomLogticks() has yet to be implemented in plotly.
If you'd like to see this geom implemented,
Please open an issue with your example code at
https://github.com/ropensci/plotly/issuesplotly.js does not (yet) support horizontal legend items
You can track progress here:
https://github.com/plotly/plotly.js/issues/53
ggplotly(ggplot(data=PIC, aes(x=Strain, y=SinkVel)) + geom_boxplot()+theme_Publication())
ggplot(data=PIC, aes(x=PICpercellpg, y=beta_d, color=Strain)) + geom_point(size=5)+theme_Publication()+
labs(y = expression(beta~("Encounters"~cm^3~day^-1)), x = expression("PIC"~cell^-1)) +
scale_y_log10(
breaks = scales::trans_breaks("log10", function(x) 10^x, n=2),
labels = scales::trans_format("log10", scales::math_format(10^.x))) + annotation_logticks(sides="l")

#7. calculate beta and encounters
#beta are in cells cm3/ day then encounters are to cells/cm3 day
PIC$E_DS_HV <- (PIC$beta_d*virnum*hostnum) #E calculated with Virus and Host (10:1 MOI)
PIC$E_DS_V <- (PIC$beta_d*virnum) #E calculated with Virus
#8. calculate for lith parameters
lithvol <- 3*1e-12 #in cm3, from CJ's paper
PIC$perlith <- PIC$PICpercell/20 #in g, assuming 20 liths attached
PIC$perlithpg <- PIC$perlith*1e12 #in pg
PIC$Denlith <- (PIC$perlith/lithvol) + Den_OcM #in g/cm3, with organic matter attached
rad_lith <- 2E-6 #in m radius
PIC$SinkVel_lith <- ((2*((rad_lith*100)^2)*(981)*(PIC$Denlith-Den_CH2O))/(9*(mu*10)))*864 #meter per day
PIC$beta_s_lith <- pi*(((rad_lith+Rehv)*100)^2)*(abs((PIC$SinkVel_lith-Ehv_SinkVel)/864)) #in encounters cm3/s
PIC$beta_d_lith <- PIC$beta_s_lith*86400 #in cm3/day
ggplot(data=PIC, aes(x=perlithpg, y=SinkVel_lith, color=Strain, shape=group)) + geom_point(size=5)+theme_Publication()+
labs(y = expression("Sinking velocity"~("m"~day^-1)), x = expression("PIC"~lith^-1))

PIC$Elith_DS_HV <- (PIC$beta_d_lith*virnum*hostnum) #E calculated with Virus and Host (10:1 MOI)
PIC$Elith_DS_V <- (PIC$beta_d_lith*virnum) #E calculated with Virus
require (dplyr)
PIC$group2 <- case_when(
PIC$PICpercellpg <2 ~ "naked_bouyant",
PIC$PICpercellpg >2 & PIC$PICpercellpg < 4 ~ "naked/calcified uncertain",
PIC$PICpercellpg >4 & PIC$PICpercellpg < 10 ~ "moderately calcified",
PIC$PICpercellpg >10 ~ "strongly calcified",
TRUE ~ as.character(PIC$PICpercellpg)
)
breaks <- 10^(-10:10)
ggplot(data=PIC, aes(x=SinkVel, y=E_DS_HV, color=Strain, shape=group)) + geom_point(size=5)+theme_Publication()+
scale_y_log10(
breaks = scales::trans_breaks("log10", function(x) 10^x, n=2),
labels = scales::trans_format("log10", scales::math_format(10^.x))) + annotation_logticks(sides="l")

ggplot(data=PIC, aes(x=SinkVel, y=E_DS_V, color=Strain, shape=group)) + geom_point(size=5)+theme_Publication()+
scale_y_log10(
breaks = scales::trans_breaks("log10", function(x) 10^x, n=2),
labels = scales::trans_format("log10", scales::math_format(10^.x))) + annotation_logticks(sides="l")

ggplot(data=PIC, aes(x=SinkVel_lith, y=Elith_DS_V, color=Strain, shape=group)) + geom_point(size=5) +
theme_Publication() + scale_y_log10(
breaks = scales::trans_breaks("log10", function(x) 10^x, n=3),
labels = scales::trans_format("log10", scales::math_format(10^.x))) + annotation_logticks(sides="l")

summary_DS <- ddply(PIC, .(Strain), summarize, PICpercellpg=mean(PICpercellpg), perlithpg = mean(perlithpg),
Den_celltotal = mean (Den_celltotal),
SinkVel=mean(SinkVel),beta_d=mean(beta_d), E_DS_V= mean(E_DS_V), E_DS_HV=mean(E_DS_HV),
SinkVel_lith=mean (SinkVel_lith), beta_d_lith=mean (beta_d_lith), Elith_DS_HV=mean (Elith_DS_HV),
Elith_DS_V=mean (Elith_DS_V))
summary_DS_bygroup <- ddply(PIC, .(group2), summarize, PICpercellpg=mean(PICpercellpg), perlithpg = mean(perlithpg),
Den_celltotal = mean (Den_celltotal),
SinkVel=mean(SinkVel),beta_d=mean(beta_d), E_DS_V= mean(E_DS_V), E_DS_HV=mean(E_DS_HV),
SinkVel_lith=mean (SinkVel_lith), beta_d_lith=mean (beta_d_lith),
Elith_DS_HV=mean (Elith_DS_HV), Elith_DS_V=mean (Elith_DS_V))
summary_DS
summary_DS_bygroup
setwd("D:/R program")
The working directory was changed to D:/R program inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the working directory for notebook chunks.
require(openxlsx)
write.xlsx(summary_DS, file = "Postdoc-R/Exported Tables/summary_DS.xlsx")
write.xlsx(summary_DS_bygroup, file = "Postdoc-R/Exported Tables/summary_DS_bygroup.xlsx")
#9. regression of PIC and sinkvel of cells and liths
#a. for cells
PIC_reg <- lm(SinkVel~PICpercellpg, data=PIC) #essentially perfect fit: summary may be unreliable haha
summary(PIC_reg)
Call:
lm(formula = SinkVel ~ PICpercellpg, data = PIC)
Residuals:
Min 1Q Median 3Q Max
-0.0115036 -0.0029329 0.0004329 0.0021756 0.0113796
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.0180085 0.0009620 18.72 <2e-16 ***
PICpercellpg 0.0177476 0.0001254 141.58 <2e-16 ***
---
Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1
Residual standard error: 0.005175 on 44 degrees of freedom
Multiple R-squared: 0.9978, Adjusted R-squared: 0.9978
F-statistic: 2.004e+04 on 1 and 44 DF, p-value: < 2.2e-16
plot(residuals.lm(PIC_reg))
layout(matrix(1:4,2,2))

plot(PIC_reg)

coef(PIC_reg)
(Intercept) PICpercellpg
0.01800852 0.01774764
# coef(PIC_reg)
#(Intercept) PICpercellpg
#0.01800852 0.01774764
cor(PIC$PICpercellpg, PIC$SinkVel)
[1] 0.9989042
#cor = 0.9989042
beta_reg <- lm(beta_d~PICpercellpg, data=PIC)
plot(residuals.lm(beta_reg))
coef(beta_reg)
(Intercept) PICpercellpg
1.639233e-07 3.243054e-07
#coef(beta_reg)
# (Intercept) PICpercellpg
#1.639233e-07 3.243054e-07
E_DS_HV_reg <- lm(E_DS_HV~PICpercellpg, data=PIC)
E_DS_V_reg <- lm(E_DS_V~PICpercellpg, data=PIC)
plot(residuals.lm(E_DS_HV_reg))
plot(residuals.lm(E_DS_V_reg))
coef(E_DS_HV_reg)
(Intercept) PICpercellpg
1.639233 3.243054
coef(E_DS_V_reg)
(Intercept) PICpercellpg
0.001639233 0.003243054
#b. for liths
perlith_reg <- lm (perlithpg~PICpercellpg, data=PIC)
plot(resid(perlith_reg))

coef(perlith_reg)
(Intercept) PICpercellpg
-6.547738e-17 5.000000e-02
sinkvel_lith_reg <- lm(SinkVel_lith~PICpercellpg, data = PIC)
summary(sinkvel_lith_reg)
essentially perfect fit: summary may be unreliable
Call:
lm(formula = SinkVel_lith ~ PICpercellpg, data = PIC)
Residuals:
Min 1Q Median 3Q Max
-1.043e-16 -3.686e-17 -2.422e-18 4.040e-17 7.364e-17
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.673e-02 8.512e-18 1.965e+15 <2e-16 ***
PICpercellpg 1.115e-02 1.109e-18 1.005e+16 <2e-16 ***
---
Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1
Residual standard error: 4.579e-17 on 44 degrees of freedom
Multiple R-squared: 1, Adjusted R-squared: 1
F-statistic: 1.011e+32 on 1 and 44 DF, p-value: < 2.2e-16
plot(residuals.lm(sinkvel_lith_reg))
layout(matrix(1:4,2,2))

plot(sinkvel_lith_reg)

coef(sinkvel_lith_reg)
(Intercept) PICpercellpg
0.01672753 0.01115169
beta_lith_reg <- lm(beta_d_lith~PICpercellpg, data=PIC)
plot(residuals.lm(beta_lith_reg))
coef(beta_lith_reg)
(Intercept) PICpercellpg
2.302277e-07 1.528542e-07
Elith_DS_HV_reg <- lm(Elith_DS_HV~PICpercellpg, data=PIC)
Elith_DS_V_reg <- lm(Elith_DS_V~PICpercellpg, data=PIC)
plot(residuals.lm(Elith_DS_HV_reg))
plot(residuals.lm(Elith_DS_V_reg))

coef(Elith_DS_HV_reg)
(Intercept) PICpercellpg
2.302277 1.528542
coef(Elith_DS_V_reg)
(Intercept) PICpercellpg
0.002302277 0.001528542
# 9. make new dataframe depending on experimental PIC values
# make a prediction based on PIC values
require(truncnorm)
require(Rmisc)
summary(PIC$PICpercellpg)
Min. 1st Qu. Median Mean 3rd Qu. Max.
-1.5873 0.5523 2.3009 4.6739 6.1204 20.1442
summarySE(data=PIC, measurevar="PICpercellpg")
PIC_newdata <- as.data.frame(rtruncnorm(n=1000, a=-1.6, b=20.14, mean=4.7, sd=6.15))
#rename column. rename function in plyr
library(plyr)
PIC_newdata <- rename (PIC_newdata, c ("rtruncnorm(n = 1000, a = -1.6, b = 20.14, mean = 4.7, sd = 6.15)" =
"PICpercellpg"))
PIC_newdata <- mutate(PIC_newdata, group = ifelse(PICpercellpg < 4 , "naked", "calcified"))
ggplotly(ggplot(data=PIC_newdata, aes(x=group, y=PICpercellpg)) + geom_boxplot()+geom_point(size=2) +
theme_Publication())
PIC_newdata$group2 <- case_when(
PIC_newdata$PICpercellpg <2 ~ "naked_bouyant",
PIC_newdata$PICpercellpg >2 & PIC_newdata$PICpercellpg < 4 ~ "naked/calcified uncertain",
PIC_newdata$PICpercellpg >4 & PIC_newdata$PICpercellpg < 10 ~ "moderately calcified",
PIC_newdata$PICpercellpg >10 ~ "strongly calcified",
TRUE ~ as.character(PIC_newdata$PICpercellpg)
)
ggplotly(ggplot(data=PIC_newdata, aes(x=group2, y=PICpercellpg)) +
geom_boxplot()+geom_point(size=2) +theme_Publication())
#a. for host
PIC_newdata$SinkVel.pred <- predict(PIC_reg, data.frame(PIC_newdata))
PIC_newdata_reg <- lm(SinkVel.pred~PICpercellpg, data=PIC_newdata)
coef(PIC_newdata_reg)
(Intercept) PICpercellpg
0.01800852 0.01774764
#same coef as PIC_reg
#> coef(PIC_newdata_reg)
#(Intercept) PICpercellpg
#0.01800852 0.01774764
plot(resid(PIC_newdata_reg))

ggplot(data=PIC_newdata, aes(x=PICpercellpg, y=SinkVel.pred)) +geom_point(size=2) +theme_Publication()+
labs(y = expression("Predicted Sinking velocity"~("m"~day^-1)), x = expression("PIC"~cell^-1))

PIC_newdata$beta.pred <- predict(beta_reg, data.frame(PIC_newdata))
PIC_newdata$E_DS_V.pred <- predict(E_DS_V_reg, data.frame(PIC_newdata))
PIC_newdata$E_DS_HV.pred <- predict(E_DS_HV_reg, data.frame(PIC_newdata))
ggplot(data=PIC_newdata, aes(x=PICpercellpg, y=E_DS_V.pred)) +geom_point(size=5, aes(color=group2)) +
theme_Publication() + geom_smooth() +
scale_y_log10(
breaks = scales::trans_breaks("log10", function(x) 10^x, n=4),
labels = scales::trans_format("log10", scales::math_format(10^.x))) + annotation_logticks(sides="l")

PICbeta_new <- ggplot(data=PIC_newdata, aes(x=PICpercellpg, y=beta.pred)) +
geom_point(size=5, aes(color=PICpercellpg))+
scale_colour_gradient(name="PIC", guide=guide_colorbar(direction = "vertical", barheight=10))+
theme_Publication() +
scale_y_log10(
breaks = scales::trans_breaks("log10", function(x) 10^x, n=4),
labels = scales::trans_format("log10", scales::math_format(10^.x))) + annotation_logticks(sides="l") +
labs(y = expression(beta~("Predicted Encounters"~cm^3~day^-1)), x = expression("PIC"~cell^-1))+
theme(legend.position = "right")
PICbeta_new

#b. for liths
PIC_newdata$perlithpg.pred <- predict(perlith_reg, data.frame(PIC_newdata))
PIC_newdata$SinkVel.pred.lith <- predict(sinkvel_lith_reg, data.frame(PIC_newdata))
sinkvel_lith_reg.pred <- lm(SinkVel.pred.lith~PICpercellpg, data=PIC_newdata)
coef(sinkvel_lith_reg.pred)
(Intercept) PICpercellpg
0.01672753 0.01115169
#same coef as sinkvel_lith_reg
#> coef(sinkvel_lith_reg.pred)
#(Intercept) PICpercellpg
# 0.01672753 0.01115169
plot(resid(sinkvel_lith_reg.pred))

ggplot(data=PIC_newdata, aes(x=perlithpg.pred, y=SinkVel.pred.lith)) +geom_point(size=2) +theme_Publication()+
labs(y = expression("Predicted Sinking velocity of Liths"~("m"~day^-1)), x = expression("PIC"~lith^-1))

PIC_newdata$beta.pred.lith <- predict(beta_lith_reg, data.frame(PIC_newdata))
PIC_newdata$E_DS_V.pred.lith <- predict(Elith_DS_HV_reg, data.frame(PIC_newdata))
PIC_newdata$E_DS_HV.pred.lith <- predict(Elith_DS_HV_reg, data.frame(PIC_newdata))
ggplot(data=PIC_newdata, aes(x=perlithpg.pred, y=E_DS_V.pred.lith)) +geom_point(size=5, aes(color=group2)) +
theme_Publication() + scale_y_log10(
breaks = scales::trans_breaks("log10", function(x) 10^x, n=4),
labels = scales::trans_format("log10", scales::math_format(10^.x))) + annotation_logticks(sides="l")+ geom_smooth()

PICbeta_new.lith <- ggplot(data=PIC_newdata, aes(x=perlithpg.pred, y=beta.pred.lith)) +
geom_point(size=5, aes(color=PICpercellpg))+
scale_colour_gradient(name="PIC", guide=guide_colorbar(direction = "vertical", barheight=10))+
theme_Publication() +
scale_y_log10(
breaks = scales::trans_breaks("log10", function(x) 10^x, n=4),
labels = scales::trans_format("log10", scales::math_format(10^.x))) + annotation_logticks(sides="l") +
labs(y = expression(beta~("Predicted Encounters of lith " ~cm^3~day^-1)), x = expression("PIC"~lith^-1))+
theme(legend.position = "right")
PICbeta_new.lith

#summaries
summary_DS_newdata_bygroup2 <- ddply(PIC_newdata, .(group2), summarize, PICpercellpg=mean(PICpercellpg), SinkVel.pred=mean(SinkVel.pred),beta.pred= mean (beta.pred), E_DS_V.pred= mean(E_DS_V.pred), E_DS_HV.pred=mean(E_DS_HV.pred))
summary_DS_bygroup.pred <- ddply(PIC_newdata, .(group2), summarize, PICpercellpg=mean(PICpercellpg),
perlithpg.pred = mean(perlithpg.pred), SinkVel.pred=mean(SinkVel.pred),
beta.pred= mean (beta.pred), E_DS_V.pred= mean(E_DS_V.pred),
E_DS_HV.pred=mean(E_DS_HV.pred),
SinkVel.pred.lith=mean(SinkVel.pred.lith),beta.pred.lith= mean (beta.pred.lith),
E_DS_V.pred.lith= mean(E_DS_V.pred.lith), E_DS_HV.pred.lith=mean(E_DS_HV.pred.lith))
summary_DS_bygroup.pred
write.xlsx(summary_DS_bygroup.pred, file = "Postdoc-R/Exported Tables/summary_DS_bygroup.pred.xlsx")
cannot create file 'Postdoc-R/Exported Tables/summary_DS_bygroup.pred.xlsx', reason 'No such file or directory'
turbulence
#TURBULENCE
#disrate is cm2/s3
#make data frame
disrate <- rep_len(10^(-8:-2), length.out=14)
calc <- rep_len(c("calcified"), length.out=7)
naked <- rep_len(c("naked"), length.out=7)
lith <- rep_len(c("lith"), length.out=7)
group <- c(calc, naked, lith)
turb <- as.data.frame(cbind(disrate, group))
number of rows of result is not a multiple of vector length (arg 1)
turb$rad <- case_when(
turb$group =="naked" ~ 1.8E-6,
turb$group =="calcified" ~ 2.3E-6,
turb$group =="lith" ~ 2E-6,
TRUE ~ as.numeric(turb$group)
)
#turb <- mutate(turb, rad = ifelse(group == "naked" , 1.8E-6, 2.3E-6)) #in m
turb$disrate <- as.numeric(as.character(turb$disrate))
turb$Kol <- ((v^3/turb$disrate)^0.25)*100 #Kolmogorov length scale in cm
#everything is below 1 cm, use eqn 2 in TK
turb$beta_d <- (4.2*pi*((turb$disrate/(v*100^2))^0.5)*(((turb$rad+Rehv)*100)^3))*86400
turb$beta_Heidi <- (0.42*pi*((turb$disrate/(v*100^2))^0.5)*(((turb$rad+Rehv)*100)^3))*86400
#check encounters
#use TK, in cm3 s
turb$E_turb_HV <- (turb$beta_d*hostnum*virnum) #E calculated with Virus and Host (10:1 MOI)
turb$E_turb_V <- (turb$beta_d*virnum) #E calculated with virus only
#breaks <- 10^(-10:10)
#minor_breaks <- rep(1:9, 21)*(10^rep(-10:10, each=9))
ggplot(data = turb, aes(x = disrate, y = beta_d, color=group)) + geom_point(size =5) +
scale_y_log10(
breaks = scales::trans_breaks("log10", function(x) 10^x, n=4),
labels = scales::trans_format("log10", scales::math_format(10^.x))) +
scale_x_log10(
breaks = scales::trans_breaks("log10", function(x) 10^x, n=4),
labels = scales::trans_format("log10", scales::math_format(10^.x))) +
annotation_logticks() +
theme_Publication()

library(scales)
ggplot(data = turb, aes(x = disrate, y = E_turb_V, color=group)) + geom_point(size =5) +
scale_y_log10(
breaks = scales::trans_breaks("log10", function(x) 10^x, n=4),
labels = scales::trans_format("log10", scales::math_format(10^.x))) +
scale_x_log10(
breaks = scales::trans_breaks("log10", function(x) 10^x, n=4),
labels = scales::trans_format("log10", scales::math_format(10^.x))) +
annotation_logticks()+
theme_Publication()

add beta kernels and plot
#extract mean betas from PIC_newdata
beta_DS <- summarySE (PIC_newdata, measurevar = "beta.pred", groupvars = c("group", "group2"))
lith_DS <-summarySE (PIC_newdata, measurevar = "beta.pred.lith", groupvars = c("group", "group2"))
all <- Reduce(function(x,y) merge(x,y,by="group",all=TRUE) ,list(BM, beta_DS, lith_DS, turb))
#beta_d.x=BM, beta.pred=DS, betapred.lith= beta.pred.lith, beta_d.y=turb
#rename beta.pred to beta_pred so I can use grep.
#all <- rename (all, c("beta_d.x" = "beta_BM", "beta.pred" = "beta_DS", "beta_d.y" = "beta_turb"))
library(data.table)
NT = data.table(all, key="group")
allbetas = NT[, list(group=group, disrate=disrate, beta_BM=beta_d.x, beta_DS=beta.pred, beta_turb = beta_d.y,
beta_BM_DS =beta_d.x + beta.pred,
beta_DS_turb = beta.pred + beta_d.y,
beta_DS_turb.lith = beta.pred.lith + beta_d.y,
beta_BM_turb = beta_d.x + beta_d.y,
beta_all = beta_d.x + beta_d.y + beta.pred),
by=c("group2")]
Error in eval(bysub, x, parent.frame()) : object 'group2' not found
---
title: "beta kernel final notebook"
output: html_notebook
---

This is an [R Markdown](http://rmarkdown.rstudio.com) Notebook. When you execute code within the notebook, the results appear beneath the code. 

Try executing this chunk by clicking the *Run* button within the chunk or by placing your cursor inside it and pressing *Ctrl+Shift+Enter*. 

Add a new chunk by clicking the *Insert Chunk* button on the toolbar or by pressing *Ctrl+Alt+I*.

When you save the notebook, an HTML file containing the code and output will be saved alongside it (click the *Preview* button or press *Ctrl+Shift+K* to preview the HTML file).

The preview shows you a rendered HTML copy of the contents of the editor. Consequently, unlike *Knit*, *Preview* does not run any R code chunks. Instead, the output of the chunk when it was last run in the editor is displayed.

```{r}
setwd("D:/R program")
#values needed 

K= 1.38064852*(10)^-23 #m2 kg/ s2 K boltzmann constant
mu= 1.126*(10)^-3 #kg/m s dynamic viscosity in 18C
v= 1.099*(10)^-6 #m2/s kinematic viscosity in 18C
Reh_calc= 2.3E-6 #in m radius Ehux
Reh_naked= 1.8E-6 #in m radius Ehux
Rehv= 90*(10)^-9 #in m radius virus
Temp = 18+273.15 #temp in kelvin, here assuming 18C
Den_OcM = 1.05 #g/cm3 density organic cell matter
Den_CH2O= 1.025 #g/cm3 density seawater at 18C
hostnum <- (10)^3
virnum <- (10)^4

require (ggplot2)
require(plotly)
require(grid)
require(ggthemes)
require (dplyr)
require(plyr)
source ("theme_Publication.R")
source("resizewin.R")
resize.win(12,9)
grid.newpage()

```

for Brownian motion

```{r}
#Brownian motion (BM)
#1. make a data frame
BM <- data.frame (group= c("naked", "calcified"), rad= c(1.8E-6, 2.3E-6)) 

#2. calculate beta (beta)
BM$beta_s <- (2*(K*(10)^4)*Temp*(((BM$rad+Rehv)*100)^2))/((3*mu*10)*(BM$rad*Rehv*1e4)) #m3/s
BM$beta_d <- BM$beta_s*86400 #to cm3/day

# go back to this later
#3. calculate encounters (E)
BM$E <- BM$beta_d*hostnum
BM$E_HV <- BM$beta_d*virnum*hostnum

BM
```

Differential settling (DS)

```{r}
#Differential settling (DS)
#1. read in PIC data
library(readr) #always use readr not baseR

setwd("D:/R program")
PIC <- read_csv("Postdoc-R/CSV Files/PIC.csv")

PIC$Strain <- as.factor(PIC$Strain)
PIC$Replicate <- as.factor(PIC$Replicate)

#certain changes in data.table API made calculating inside the list data.table to not work

#2. calculate PIC
PIC$PIC <- PIC$TC-PIC$AC
PIC$PICpercell <- (PIC$PIC/PIC$Cellcount)*(10)^-3#in g
PIC$PICpercellpg <- PIC$PICpercell*1e12

ggplotly(ggplot(data=PIC, aes(x=Strain, y=PICpercellpg)) + geom_boxplot()+geom_point(size=2) +theme_Publication())

```

```{r}

#3. calculate density of cells (den)
PIC <- mutate(PIC, group = ifelse(PICpercellpg < 4 , "naked", "calcified"))

ggplotly(ggplot(data=PIC, aes(x=group, y=PICpercellpg)) + geom_boxplot()+geom_point(size=2, aes(color=Strain))+
           theme_Publication())

PIC <- mutate(PIC, rad = ifelse(group == "naked" ,  1.8E-6,  2.3E-6)) #in m

PIC$volume <- (4/3)*pi*(PIC$rad*100)^3 #in cm3
PIC$Den_cell <- PIC$PICpercell/PIC$volume #g/cm3
PIC$Den_celltotal <- PIC$Den_cell+Den_OcM

ggplotly(ggplot(data=PIC, aes(x=Strain, y=Den_celltotal, color=group)) + geom_boxplot()+geom_point(size=2) 
         +theme_Publication())

#some strains that are "naked" have PIC<2. I chose to ignore this since in the lm model I do not use
#strain as a factor, rather data is treated as a whole (e.g., no grouping)

#4. calculate sinking velocity of cells
PIC$SinkVel <- ((2*((PIC$rad*100)^2)*(981)*(PIC$Den_celltotal-Den_CH2O))/(9*(mu*10)))*864 #meter per day

#g is converted to per day, 864 is the one that converts cm/s to m/day

#plot sinking velocity vs calcification

ggplot(data=PIC, aes(x=PICpercellpg, y=SinkVel, color=Strain, shape=group)) + geom_point(size=5)+theme_Publication()+
    labs(y = expression("Sinking velocity"~("m"~day^-1)), x = expression("PIC"~cell^-1)) +
    scale_y_log10(
        breaks = scales::trans_breaks("log10", function(x) 10^x, n=2),
        labels = scales::trans_format("log10", scales::math_format(10^.x))) + annotation_logticks(sides="l")

#5. calculate sinkvel of viruses

Den_virus <- 1.09 #data from Ben D. fresh EhV-207 density. old density of EhV-207 is 1.19
Ehv_SinkVel <- ((2*((Rehv*100)^2)*(981)*(Den_virus-Den_CH2O))/(9*(mu*10)))*864  #equals to 0

#6. calculate beta kernels
PIC$beta_s <- pi*(((PIC$rad+Rehv)*100)^2)*(abs((PIC$SinkVel-Ehv_SinkVel)/864)) #in encounters cm3/s
PIC$beta_d <- PIC$beta_s*86400 #in cm3/day

Sinkvelbeta.plot<- ggplot(data=PIC, aes(x=SinkVel, y=beta_d, color=Strain, shape=group)) + geom_point(size=5)+
  theme_Publication()+
  labs(x = expression("Sinking velocity"~("m"~day^-1)), y = expression(beta~("Encounters" ~ cm^3~day^-1))) +
 scale_y_log10(
        breaks = scales::trans_breaks("log10", function(x) 10^x, n=2),
        labels = scales::trans_format("log10", scales::math_format(10^.x))) + annotation_logticks(sides="l")

Sinkvelbeta.plot #change ticks

ggplotly(Sinkvelbeta.plot)

ggplotly(ggplot(data=PIC, aes(x=Strain, y=SinkVel)) + geom_boxplot()+theme_Publication())

ggplot(data=PIC, aes(x=PICpercellpg, y=beta_d, color=Strain)) + geom_point(size=5)+theme_Publication()+
  labs(y = expression(beta~("Encounters"~cm^3~day^-1)), x = expression("PIC"~cell^-1))  +
scale_y_log10(
        breaks = scales::trans_breaks("log10", function(x) 10^x, n=2),
        labels = scales::trans_format("log10", scales::math_format(10^.x))) + annotation_logticks(sides="l")

```

```{r}
#7. calculate beta and encounters
#beta are in cells cm3/ day then encounters are to cells/cm3 day
PIC$E_DS_HV <- (PIC$beta_d*virnum*hostnum)  #E calculated with Virus and Host (10:1 MOI)
PIC$E_DS_V <- (PIC$beta_d*virnum) #E calculated with Virus

#8. calculate for lith parameters

lithvol <- 3*1e-12 #in cm3, from CJ's paper
PIC$perlith <- PIC$PICpercell/20 #in g, assuming 20 liths attached
PIC$perlithpg <- PIC$perlith*1e12 #in pg
PIC$Denlith <- (PIC$perlith/lithvol) + Den_OcM #in g/cm3, with organic matter attached
rad_lith <- 2E-6 #in m radius

PIC$SinkVel_lith <- ((2*((rad_lith*100)^2)*(981)*(PIC$Denlith-Den_CH2O))/(9*(mu*10)))*864 #meter per day
PIC$beta_s_lith <- pi*(((rad_lith+Rehv)*100)^2)*(abs((PIC$SinkVel_lith-Ehv_SinkVel)/864)) #in encounters cm3/s
PIC$beta_d_lith <- PIC$beta_s_lith*86400 #in cm3/day

ggplot(data=PIC, aes(x=perlithpg, y=SinkVel_lith, color=Strain, shape=group)) + geom_point(size=5)+theme_Publication()+
  labs(y = expression("Sinking velocity"~("m"~day^-1)), x = expression("PIC"~lith^-1)) 

PIC$Elith_DS_HV <- (PIC$beta_d_lith*virnum*hostnum)  #E calculated with Virus and Host (10:1 MOI)
PIC$Elith_DS_V <- (PIC$beta_d_lith*virnum) #E calculated with Virus

require (dplyr)

PIC$group2 <- case_when(
  PIC$PICpercellpg <2  ~ "naked_bouyant",
  PIC$PICpercellpg >2 & PIC$PICpercellpg < 4 ~ "naked/calcified uncertain",
  PIC$PICpercellpg >4 & PIC$PICpercellpg < 10 ~ "moderately calcified",
  PIC$PICpercellpg >10 ~ "strongly calcified", 
  TRUE ~ as.character(PIC$PICpercellpg)
)

breaks <- 10^(-10:10)

ggplot(data=PIC, aes(x=SinkVel, y=E_DS_HV, color=Strain, shape=group)) + geom_point(size=5)+theme_Publication()+
 scale_y_log10(
        breaks = scales::trans_breaks("log10", function(x) 10^x, n=2),
        labels = scales::trans_format("log10", scales::math_format(10^.x))) + annotation_logticks(sides="l")


ggplot(data=PIC, aes(x=SinkVel, y=E_DS_V, color=Strain, shape=group)) + geom_point(size=5)+theme_Publication()+
  scale_y_log10(
        breaks = scales::trans_breaks("log10", function(x) 10^x, n=2),
        labels = scales::trans_format("log10", scales::math_format(10^.x))) + annotation_logticks(sides="l")


ggplot(data=PIC, aes(x=SinkVel_lith, y=Elith_DS_V, color=Strain, shape=group)) + geom_point(size=5) + 
  theme_Publication() + scale_y_log10(
        breaks = scales::trans_breaks("log10", function(x) 10^x, n=3),
        labels = scales::trans_format("log10", scales::math_format(10^.x))) + annotation_logticks(sides="l")



summary_DS <- ddply(PIC, .(Strain), summarize,  PICpercellpg=mean(PICpercellpg), perlithpg = mean(perlithpg), 
                    Den_celltotal = mean (Den_celltotal),
                    SinkVel=mean(SinkVel),beta_d=mean(beta_d), E_DS_V= mean(E_DS_V), E_DS_HV=mean(E_DS_HV),
                    SinkVel_lith=mean (SinkVel_lith), beta_d_lith=mean (beta_d_lith), Elith_DS_HV=mean (Elith_DS_HV),
                    Elith_DS_V=mean (Elith_DS_V))

summary_DS_bygroup <- ddply(PIC, .(group2), summarize,  PICpercellpg=mean(PICpercellpg), perlithpg = mean(perlithpg), 
                            Den_celltotal = mean (Den_celltotal),
                            SinkVel=mean(SinkVel),beta_d=mean(beta_d), E_DS_V= mean(E_DS_V), E_DS_HV=mean(E_DS_HV), 
                            SinkVel_lith=mean (SinkVel_lith), beta_d_lith=mean (beta_d_lith), 
                            Elith_DS_HV=mean (Elith_DS_HV), Elith_DS_V=mean (Elith_DS_V))

summary_DS
summary_DS_bygroup

setwd("D:/R program")
require(openxlsx)
write.xlsx(summary_DS, file = "Postdoc-R/Exported Tables/summary_DS.xlsx")
write.xlsx(summary_DS_bygroup, file = "Postdoc-R/Exported Tables/summary_DS_bygroup.xlsx")

```

```{r}

#9. regression of PIC and sinkvel of cells and liths

#a. for cells
PIC_reg <- lm(SinkVel~PICpercellpg, data=PIC) #essentially perfect fit: summary may be unreliable haha
summary(PIC_reg)
plot(residuals.lm(PIC_reg))
layout(matrix(1:4,2,2))
plot(PIC_reg)

coef(PIC_reg)
# coef(PIC_reg)
#(Intercept) PICpercellpg 
#0.01800852   0.01774764 

cor(PIC$PICpercellpg, PIC$SinkVel)
#cor = 0.9989042

beta_reg <- lm(beta_d~PICpercellpg, data=PIC)
plot(residuals.lm(beta_reg))
coef(beta_reg)
#coef(beta_reg)
# (Intercept) PICpercellpg 
#1.639233e-07 3.243054e-07

E_DS_HV_reg <- lm(E_DS_HV~PICpercellpg, data=PIC)
E_DS_V_reg <- lm(E_DS_V~PICpercellpg, data=PIC)
plot(residuals.lm(E_DS_HV_reg))
plot(residuals.lm(E_DS_V_reg))
coef(E_DS_HV_reg)
coef(E_DS_V_reg)

#b. for liths
perlith_reg <- lm (perlithpg~PICpercellpg, data=PIC)
plot(resid(perlith_reg))
coef(perlith_reg)
sinkvel_lith_reg <- lm(SinkVel_lith~PICpercellpg, data = PIC)
summary(sinkvel_lith_reg)
plot(residuals.lm(sinkvel_lith_reg))
layout(matrix(1:4,2,2))
plot(sinkvel_lith_reg)
coef(sinkvel_lith_reg)

beta_lith_reg <- lm(beta_d_lith~PICpercellpg, data=PIC)
plot(residuals.lm(beta_lith_reg))
coef(beta_lith_reg)

Elith_DS_HV_reg <- lm(Elith_DS_HV~PICpercellpg, data=PIC)
Elith_DS_V_reg <- lm(Elith_DS_V~PICpercellpg, data=PIC)
plot(residuals.lm(Elith_DS_HV_reg))
plot(residuals.lm(Elith_DS_V_reg))
coef(Elith_DS_HV_reg)
coef(Elith_DS_V_reg)

```

```{r}

# 9. make new dataframe depending on experimental PIC values
# make a prediction based on PIC values
require(truncnorm)
require(Rmisc)
summary(PIC$PICpercellpg)
summarySE(data=PIC, measurevar="PICpercellpg")
PIC_newdata <- as.data.frame(rtruncnorm(n=1000, a=-1.6, b=20.14, mean=4.7, sd=6.15))

#rename column. rename function in plyr 
library(plyr)
PIC_newdata <- rename (PIC_newdata, c ("rtruncnorm(n = 1000, a = -1.6, b = 20.14, mean = 4.7, sd = 6.15)" = 
                                         "PICpercellpg"))

PIC_newdata <- mutate(PIC_newdata, group = ifelse(PICpercellpg < 4 , "naked", "calcified"))

ggplotly(ggplot(data=PIC_newdata, aes(x=group, y=PICpercellpg)) + geom_boxplot()+geom_point(size=2) +
           theme_Publication())

PIC_newdata$group2 <- case_when(
  PIC_newdata$PICpercellpg <2  ~ "naked_bouyant",
  PIC_newdata$PICpercellpg >2 & PIC_newdata$PICpercellpg < 4 ~ "naked/calcified uncertain",
  PIC_newdata$PICpercellpg >4 & PIC_newdata$PICpercellpg < 10 ~ "moderately calcified",
  PIC_newdata$PICpercellpg >10 ~ "strongly calcified", 
  TRUE ~ as.character(PIC_newdata$PICpercellpg)
)

ggplotly(ggplot(data=PIC_newdata, aes(x=group2, y=PICpercellpg)) + 
           geom_boxplot()+geom_point(size=2) +theme_Publication())

#a. for host
PIC_newdata$SinkVel.pred <- predict(PIC_reg, data.frame(PIC_newdata))
PIC_newdata_reg <- lm(SinkVel.pred~PICpercellpg, data=PIC_newdata) 
coef(PIC_newdata_reg)
#same coef as PIC_reg
#> coef(PIC_newdata_reg)
#(Intercept) PICpercellpg 
#0.01800852   0.01774764 

plot(resid(PIC_newdata_reg))

ggplot(data=PIC_newdata, aes(x=PICpercellpg, y=SinkVel.pred)) +geom_point(size=2) +theme_Publication()+
  labs(y = expression("Predicted Sinking velocity"~("m"~day^-1)), x = expression("PIC"~cell^-1)) 

PIC_newdata$beta.pred <- predict(beta_reg, data.frame(PIC_newdata))
PIC_newdata$E_DS_V.pred <- predict(E_DS_V_reg, data.frame(PIC_newdata))
PIC_newdata$E_DS_HV.pred <- predict(E_DS_HV_reg, data.frame(PIC_newdata))

ggplot(data=PIC_newdata, aes(x=PICpercellpg, y=E_DS_V.pred)) +geom_point(size=5, aes(color=group2)) +
  theme_Publication() + geom_smooth() +
  scale_y_log10(
        breaks = scales::trans_breaks("log10", function(x) 10^x, n=4),
        labels = scales::trans_format("log10", scales::math_format(10^.x))) + annotation_logticks(sides="l")
  

PICbeta_new <- ggplot(data=PIC_newdata, aes(x=PICpercellpg, y=beta.pred)) +
  geom_point(size=5, aes(color=PICpercellpg))+
  scale_colour_gradient(name="PIC", guide=guide_colorbar(direction = "vertical", barheight=10))+
  theme_Publication() + 
  scale_y_log10(
        breaks = scales::trans_breaks("log10", function(x) 10^x, n=4),
        labels = scales::trans_format("log10", scales::math_format(10^.x))) + annotation_logticks(sides="l") +
  labs(y = expression(beta~("Predicted Encounters"~cm^3~day^-1)), x = expression("PIC"~cell^-1))+
  theme(legend.position = "right")
PICbeta_new

#b. for liths
PIC_newdata$perlithpg.pred <- predict(perlith_reg, data.frame(PIC_newdata))
PIC_newdata$SinkVel.pred.lith <- predict(sinkvel_lith_reg, data.frame(PIC_newdata))
sinkvel_lith_reg.pred <- lm(SinkVel.pred.lith~PICpercellpg, data=PIC_newdata) 
coef(sinkvel_lith_reg.pred)
#same coef as sinkvel_lith_reg
#> coef(sinkvel_lith_reg.pred)
#(Intercept) PICpercellpg 
# 0.01672753   0.01115169 

plot(resid(sinkvel_lith_reg.pred))

ggplot(data=PIC_newdata, aes(x=perlithpg.pred, y=SinkVel.pred.lith)) +geom_point(size=2) +theme_Publication()+
  labs(y = expression("Predicted Sinking velocity of Liths"~("m"~day^-1)), x = expression("PIC"~lith^-1)) 

PIC_newdata$beta.pred.lith <- predict(beta_lith_reg, data.frame(PIC_newdata))
PIC_newdata$E_DS_V.pred.lith <- predict(Elith_DS_HV_reg, data.frame(PIC_newdata))
PIC_newdata$E_DS_HV.pred.lith <- predict(Elith_DS_HV_reg, data.frame(PIC_newdata))

ggplot(data=PIC_newdata, aes(x=perlithpg.pred, y=E_DS_V.pred.lith)) +geom_point(size=5, aes(color=group2)) +
  theme_Publication() + scale_y_log10(
        breaks = scales::trans_breaks("log10", function(x) 10^x, n=4),
        labels = scales::trans_format("log10", scales::math_format(10^.x))) + annotation_logticks(sides="l")+ geom_smooth()

PICbeta_new.lith <- ggplot(data=PIC_newdata, aes(x=perlithpg.pred, y=beta.pred.lith)) +
  geom_point(size=5, aes(color=PICpercellpg))+
  scale_colour_gradient(name="PIC", guide=guide_colorbar(direction = "vertical", barheight=10))+
  theme_Publication() + 
  scale_y_log10(
        breaks = scales::trans_breaks("log10", function(x) 10^x, n=4),
        labels = scales::trans_format("log10", scales::math_format(10^.x))) + annotation_logticks(sides="l") +
  labs(y = expression(beta~("Predicted Encounters of lith " ~cm^3~day^-1)), x = expression("PIC"~lith^-1))+
  theme(legend.position = "right")
PICbeta_new.lith

#summaries
summary_DS_newdata_bygroup2 <- ddply(PIC_newdata, .(group2), summarize,  PICpercellpg=mean(PICpercellpg), SinkVel.pred=mean(SinkVel.pred),beta.pred= mean (beta.pred), E_DS_V.pred= mean(E_DS_V.pred), E_DS_HV.pred=mean(E_DS_HV.pred))

summary_DS_bygroup.pred <- ddply(PIC_newdata, .(group2), summarize,  PICpercellpg=mean(PICpercellpg), 
                            perlithpg.pred = mean(perlithpg.pred), SinkVel.pred=mean(SinkVel.pred),
                            beta.pred= mean (beta.pred), E_DS_V.pred= mean(E_DS_V.pred), 
                            E_DS_HV.pred=mean(E_DS_HV.pred), 
                            SinkVel.pred.lith=mean(SinkVel.pred.lith),beta.pred.lith= mean (beta.pred.lith),
                            E_DS_V.pred.lith= mean(E_DS_V.pred.lith), E_DS_HV.pred.lith=mean(E_DS_HV.pred.lith))

summary_DS_bygroup.pred

write.xlsx(summary_DS_bygroup.pred, file = "Postdoc-R/Exported Tables/summary_DS_bygroup.pred.xlsx")


```

turbulence

```{r}
#TURBULENCE
#disrate is cm2/s3

#make data frame

disrate <- rep_len(10^(-8:-2), length.out=14)
calc <- rep_len(c("calcified"), length.out=7)
naked <- rep_len(c("naked"), length.out=7)
lith <- rep_len(c("lith"), length.out=7)
group <- c(calc, naked, lith)
turb <- as.data.frame(cbind(disrate, group))

turb$rad <- case_when(
    turb$group =="naked" ~ 1.8E-6,
    turb$group =="calcified" ~ 2.3E-6,
    turb$group =="lith" ~ 2E-6,
    TRUE ~ as.numeric(turb$group)
)
#turb <- mutate(turb, rad = ifelse(group == "naked" ,  1.8E-6,  2.3E-6)) #in m

turb$disrate <- as.numeric(as.character(turb$disrate))

turb$Kol <- ((v^3/turb$disrate)^0.25)*100 #Kolmogorov length scale in cm
#everything is below 1 cm, use eqn 2 in TK 

turb$beta_d <- (4.2*pi*((turb$disrate/(v*100^2))^0.5)*(((turb$rad+Rehv)*100)^3))*86400 

turb$beta_Heidi <- (0.42*pi*((turb$disrate/(v*100^2))^0.5)*(((turb$rad+Rehv)*100)^3))*86400

#check encounters

#use TK, in cm3 s
turb$E_turb_HV <- (turb$beta_d*hostnum*virnum) #E calculated with Virus and Host (10:1 MOI)
turb$E_turb_V <- (turb$beta_d*virnum) #E calculated with virus only

#breaks <- 10^(-10:10)
#minor_breaks <- rep(1:9, 21)*(10^rep(-10:10, each=9))

ggplot(data = turb, aes(x = disrate, y = beta_d, color=group)) + geom_point(size =5) +
  scale_y_log10(
        breaks = scales::trans_breaks("log10", function(x) 10^x, n=4),
        labels = scales::trans_format("log10", scales::math_format(10^.x))) +
  scale_x_log10(
        breaks = scales::trans_breaks("log10", function(x) 10^x, n=4),
        labels = scales::trans_format("log10", scales::math_format(10^.x))) +
  annotation_logticks() +
  theme_Publication()

library(scales)

ggplot(data = turb, aes(x = disrate, y = E_turb_V, color=group)) + geom_point(size =5) +
   scale_y_log10(
        breaks = scales::trans_breaks("log10", function(x) 10^x, n=4),
        labels = scales::trans_format("log10", scales::math_format(10^.x))) +
  scale_x_log10(
        breaks = scales::trans_breaks("log10", function(x) 10^x, n=4),
        labels = scales::trans_format("log10", scales::math_format(10^.x))) +
  annotation_logticks()+
  theme_Publication()

```

add beta kernels and plot

```{r}
#extract mean betas from PIC_newdata
beta_DS <- summarySE (PIC_newdata, measurevar = "beta.pred", groupvars = c("group", "group2"))
lith_DS <-summarySE (PIC_newdata, measurevar = "beta.pred.lith", groupvars = c("group", "group2"))

lith_DS$group1 <- lith_DS$group
lith_DS$group <- "lith"

#separate data frames for host and liths

all <- Reduce(function(x,y) merge(x,y,by="group",all=TRUE) ,
              list(BM, beta_DS, turb %>% filter(group %in% c("naked", "calcified"))))


all.liths  <- Reduce(function(x,y) merge(x,y,by="group",all=TRUE) ,
                     list(lith_DS, turb %>% filter(group %in% c("lith"))))

#beta_d.x=BM, beta.pred=DS, betapred.lith= beta.pred.lith, beta_d.y=turb

#rename beta.pred to beta_pred so I can use grep. 
#all <- rename (all, c("beta_d.x" = "beta_BM", "beta.pred" = "beta_DS", "beta_d.y" = "beta_turb"))

library(data.table)
NT = data.table(all, key="group2.y")
allbetas = NT[, list(group=group, disrate=disrate, beta_BM=beta_d.x, beta_DS=beta.pred, beta_turb = beta_d.y, 
                     beta_BM_DS =beta_d.x + beta.pred,
                     beta_DS_turb = beta.pred + beta_d.y,
                     beta_DS_turb.lith = beta.pred.lith + beta_d.y, 
                     beta_BM_turb = beta_d.x + beta_d.y,
                     beta_all = beta_d.x + beta_d.y + beta.pred), 
              by=c("group2")]

ggplot(allbetas, aes(disrate, y = value, color=group2)) + 
  geom_line(aes(y = beta_DS_turb, linetype = "DS+turb"), size=1) + 
  geom_line(aes(y = beta_all, linetype = "BM+DS+turb"), size=1)+
  geom_line(aes(y = beta_turb, linetype = "turb"), size=1)+
  scale_y_log10(
        breaks = scales::trans_breaks("log10", function(x) 10^x, n=4),
        labels = scales::trans_format("log10", scales::math_format(10^.x))) +
  scale_x_log10(
        breaks = scales::trans_breaks("log10", function(x) 10^x, n=7),
        labels = scales::trans_format("log10", scales::math_format(10^.x))) +
  annotation_logticks()+
  theme_Publication()

#encounters

#melt data

allbetas.melt <- melt (allbetas, id.vars = c("group2", "group", "disrate"), value.name = "beta_d", 
                       variable.name = "betakernel")

allbetas.melt$group2 <- factor (allbetas.melt$group2,levels= c("naked_bouyant", "naked/calcified uncertain",
                                                       "moderately calcified", "strongly calcified"),
                                                       labels = c("naked", "naked/calcified uncertain",
                                                                  "moderately calcified", "strongly calcified"))

allbetas.melt$E_V <- allbetas.melt$beta_d*virnum

ggplot(allbetas.melt, aes(disrate, y = E_V, color=group2)) + 
  geom_line(size=1)+
  scale_y_log10(
        breaks = scales::trans_breaks("log10", function(x) 10^x, n=4),
        labels = scales::trans_format("log10", scales::math_format(10^.x))) +
  scale_x_log10(
        breaks = scales::trans_breaks("log10", function(x) 10^x, n=7),
        labels = scales::trans_format("log10", scales::math_format(10^.x))) +
  annotation_logticks() + facet_grid(~betakernel)

#subset data

graph1 <- subset(allbetas.melt, betakernel %in% c ("beta_BM", "beta_DS", "beta_BM_DS"))
graph2 <- subset(allbetas.melt, betakernel %in% c ("beta_turb", "beta_DS_turb", "beta_BM_turb", "beta_all"))

ggplot(graph1, aes(group2, y = E_V, color=betakernel)) + 
  geom_jitter(size=5)+
  scale_y_log10(
        breaks = scales::trans_breaks("log10", function(x) 10^x, n=4),
        labels = scales::trans_format("log10", scales::math_format(10^.x))) +
  annotation_logticks(sides = "l")+
  theme_Publication()

graph1.sum <- summarySE (graph1, measurevar = "E_V", groupvars = c("betakernel", "group2"))

ggplot(graph1.sum, aes(group2, y = E_V, color=betakernel)) + 
  geom_point(size=5,  position=position_dodge(0.2))+
  scale_y_log10(
        breaks = scales::trans_breaks("log10", function(x) 10^x, n=4),
        labels = scales::trans_format("log10", scales::math_format(10^.x))) +
  annotation_logticks(sides = "l")+
  theme_Publication()

ggplot(data=allbetas.melt %>% filter(betakernel %in% c("beta_turb", "beta_all")), 
       aes(x=disrate,y = E_V, color=group2, linetype=betakernel)) + 
  geom_line(size=1, position=position_jitter(w=0.02, h=0))+
   scale_y_log10(
        breaks = scales::trans_breaks("log10", function(x) 10^x, n=4),
        labels = scales::trans_format("log10", scales::math_format(10^.x))) +
  scale_x_log10(
        breaks = scales::trans_breaks("log10", function(x) 10^x, n=7),
        labels = scales::trans_format("log10", scales::math_format(10^.x))) +
  annotation_logticks()+
  theme_Publication()

ggplot(data=allbetas.melt %>% filter(betakernel %in% c("beta_all")), 
       aes(x=disrate,y = E_V, color=group2)) + 
  geom_line(size=1, position=position_jitter(w=0.02, h=0))+
   scale_y_log10(
        breaks = scales::trans_breaks("log10", function(x) 10^x, n=2),
        labels = scales::trans_format("log10", scales::math_format(10^.x))) +
  scale_x_log10(
        breaks = scales::trans_breaks("log10", function(x) 10^x, n=7),
        labels = scales::trans_format("log10", scales::math_format(10^.x))) +
  annotation_logticks()+
  theme_Publication()

```

